1. Field of the Invention
Embodiments of the present invention generally relate to cortical networks, and more specifically, to a method and apparatus for recognizing objects visually using a recursive cortical network.
2. Description of the Related Art
Recognizing visual objects is a task that humans excel, while computers have difficulty in performing. Humans are capable of seeing an object once, such as a utensil, and then recognizing or imagining that object in other positions, contexts, or under different distortions or transformations. However, computers tend to be restricted to recognizing particular poses or sizes fed into object recognition systems. If a person views a chair from different angles or different distances, the image on the person's retina varies dramatically. Different presentations of a chair need not be similar in their “pixel” representation on the human retina for the human brain to understand and recognize an object as a chair, whereas current object recognition systems work best when there is pixel level similarity between the input image and the training images.
The human brain achieves this by storing an invariant representation of the chair. The invariant representation is used to recognize the chair in various orientations, distances, scales, transformations, lighting conditions, occlusions, and the like, while being highly selective for the identity of the object. Some object recognition systems are currently using cortex-like computations for object recognition in order to mimic the way the human brain recognizes objects. However, those systems lose accuracy when rotations, distortions, occlusions, or other transformations are applied, and are not highly selective to the objects being recognized.
Therefore, there is a need in the art for a method of object recognition to be invariant to a wide variety of transformations while being selective to the identity of the object.